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  • You Will learn basic concept of Machine Learning, Types of Machine Learning
  • You Will learn basic concept of Linear Regression With One Variable & Multiple
  • You Will learn basic concept of Logistic Regression
  • You Will learn basic concept of Regularization (Linear and Logistic Regression)
  • You Will learn basic concept of Neural Network

Steps of Machine Learning that you Will learn:

  1. Import the data.

  2. Split data into Training & Test.

  3. Create a Model.

  4. Train The Model.

  5. Make Predictions.

  6. Evaluate and improve.

Machine Learning Course Contents:

  1. What is Machine Learning - Types of Machine Learning (Supervised & Unsupervised).

  2. Linear Regression with One Variable.

  3. Linear Regression with One Variable (Cost Function - Gradient Descent).

  4. Linear Regression with Multiple Variable.

  5. Logistic Regression (Classification).

  6. Logistic Regression (Cost Function - Gradient Descent).

  7. Logistic Regression (Multiclass).

  8. Regularization Overfitting.

  9. Regularization (Linear and Logistic Regression).

  10. Neural Network Overview.

  11. Neural Network (Cost Function).

  12. Advice for Applying Machine Leaning.

  13. Machine Learning Project 1

  14. Machine Learning Project 2

Python Basics Course Contents:

  1. How to print

  2. Variables

  3. Receive Input from User

  4. Type Conversion

  5. String

  6. Formatted String

  7. String Methods

  8. Arithmetic Operations

  9. Math Functions

  10. If Statement

  11. Logical Operators

  12. Comparison Operators

  13. While

  14. For Loops

  15. Nested Loops

  16. List

  17. 2D List

  18. List Methods

  19. Tuples

  20. Unpacking

  21. Dictionaries

  22. Functions

  23. Parameters

  24. Keyword Arguments

  25. Return Statement

  26. Try - Except

  27. Comments

  28. Classes

Notes:

  1. You will Learn the basics of Machine Learning.

  2. You will learn the basics of python.

  3. You will need to setup Anaconda.

  4. You will need to setup python & PyCharm

  5. This course is considered as first step for the Machine Learning.

  6. You can ask anytime.

  7. No Programming Experience Needed for this course.

  8. Python for Data Science and Machine Learning is a great course that you can take to learn the implementation of ML models in Python.

  9. This course considered as step one in the Machine Leaning, You will learn the concept of the Machine Learning with python basics.

  • Python Setup
  • Anaconda
  • Beginner in Machine Learning
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  • Section 1 : Machine Learning with Python Basics 13 Lectures 00:16:01

    • Lecture 1 :
    • What is Machine Learning - Types of Machine Learning Preview
    • Lecture 2 :
    • Linear Regression With One Variable
    • Lecture 3 :
    • Linear Regression With One Variable (Cost Function - Gradient Descent)
    • Lecture 4 :
    • Linear Regression with Multiple Variable
    • Lecture 5 :
    • Logistic Regression (Classification)
    • Lecture 6 :
    • Logistic Regression (Cost Function - Gradient Descent)
    • Lecture 7 :
    • Logistic Regression (Multiclass)
    • Lecture 8 :
    • Regularization Overfitting
    • Lecture 9 :
    • Regularization (Linear and Logistic Regression)
    • Lecture 10 :
    • Neural Network Overview
    • Lecture 11 :
    • Neural Network (Cost Function)
    • Lecture 12 :
    • Advice for Applying Machine Leaning
    • Lecture 13 :
    • Unsupervised Machine Learning
  • Section 2 : Machine Learning Projects 3 Lectures 00:02:05

    • Lecture 1 :
    • Anaconda Setup
    • Lecture 2 :
    • Machine Learning Project 1
    • Lecture 3 :
    • Machine Learning Project 2
  • Section 3 : Python Basics 31 Lectures 01:08:20

    • Lecture 1 :
    • How to print
    • Lecture 2 :
    • Variables
    • Lecture 3 :
    • Receive Input from User
    • Lecture 4 :
    • Type Conversion
    • Lecture 5 :
    • String
    • Lecture 6 :
    • Formatted String
    • Lecture 7 :
    • String Methods
    • Lecture 8 :
    • Arithmetic Operations
    • Lecture 9 :
    • Math Functions
    • Lecture 10 :
    • If Statement
    • Lecture 11 :
    • If Statement Another Example
    • Lecture 12 :
    • Logical Operators
    • Lecture 13 :
    • Comparison Operators
    • Lecture 14 :
    • While
    • Lecture 15 :
    • For Loops
    • Lecture 16 :
    • Nested Loops
    • Lecture 17 :
    • List
    • Lecture 18 :
    • 2D List
    • Lecture 19 :
    • List Methods
    • Lecture 20 :
    • Tuples
    • Lecture 21 :
    • Unpacking
    • Lecture 22 :
    • Dictionaries
    • Lecture 23 :
    • Functions
    • Lecture 24 :
    • Parameters
    • Lecture 25 :
    • Keyword Arguments
    • Lecture 26 :
    • Return Statement
    • Lecture 27 :
    • Try - Except
    • Lecture 28 :
    • Comments
    • Lecture 29 :
    • Classes
    • Lecture 30 :
    • General Review 1
    • Lecture 31 :
    • General Review 2
  • Section 4 : Python Projects 3 Lectures 00:19:51

    • Lecture 1 :
    • Python & PyCharm Setup
    • Lecture 2 :
    • Quiz Game
    • Lecture 3 :
    • Guessing Game
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Have strong experience as CAD CAM Instructor: NX (CAD - CAM - CAE -Mold Wizard - CNC Post Builder, Sheet Metal) Solid Works, Inventor,  Solid Edge Have strong experience in post processing for CNC machines (Milling, Turning, Wire EDM) Making a (G code) programs for a lot of mechanical products and manufacturing it in CNC machines (Milling 3 Axis, Multi Axis). Have strong knowledge of mold wizard of NX (Injection Mold Plastic ) Making a complete mold base for plastic parts and get the drawings for the manufacturing.
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